






import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__		:							Tuple 				=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:							Any 				=				[
    ("""bert.bert""", """visual_bert"""),
    ("""bert.cls""", """cls"""),
    ("""bert.classifier""", """cls"""),
    ("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
    ("""position_embeddings_visual""", """visual_position_embeddings"""),
    ("""projection""", """visual_projection"""),
]

SCREAMING_SNAKE_CASE__		:							str 				=				[
    """nlvr2_coco_pre_trained.th""",
    """nlvr2_fine_tuned.th""",
    """nlvr2_pre_trained.th""",
    """vcr_coco_pre_train.th""",
    """vcr_fine_tune.th""",
    """vcr_pre_train.th""",
    """vqa_coco_pre_trained.th""",
    """vqa_fine_tuned.th""",
    """vqa_pre_trained.th""",
]





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			Union[str, Any]:

     '''simple docstring'''
     UpperCAmelCase__      :  List[Any]				      =			torch.load(lowercase_ ,							map_location="""cpu"""       )
     return sd





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=rename_keys_prefix       ) ->			List[Any]:

     '''simple docstring'''
     UpperCAmelCase__      :  List[str]				      =			OrderedDict()
     UpperCAmelCase__      :  Union[str, Any]				      =			torch.arange(config.max_position_embeddings       ).expand((1, -1)       )
     # detector_d = OrderedDict()
     for key in d:
          if "detector" in key:
               # detector_d[key.replace('detector.','')] = d[key]
               continue
          UpperCAmelCase__      :  Optional[Any]				      =			key
          for name_pair in rename_keys_prefix:
               UpperCAmelCase__      :  Any				      =			new_key.replace(name_pair[0] ,							name_pair[1]       )
          UpperCAmelCase__      :  Optional[int]				      =			d[key]
          if key == "bert.cls.predictions.decoder.weight":
               # Old bert code didn't have `decoder.bias`, but was added separately
               UpperCAmelCase__      :  int				      =			new_d["""cls.predictions.bias"""]
     return new_d





@torch.no_grad()
def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			Dict:

     '''simple docstring'''
     assert (
         checkpoint_path.split("""/"""       )[-1] in ACCEPTABLE_CHECKPOINTS
     ), F"The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."

     # Get Config
     if "pre" in checkpoint_path:
          UpperCAmelCase__      :  Union[str, Any]				      =			"""pretraining"""
          if "vcr" in checkpoint_path:
               UpperCAmelCase__      :  Dict				      =			{"""visual_embedding_dim""": 512}
          elif "vqa_advanced" in checkpoint_path:
               UpperCAmelCase__      :  Tuple				      =			{"""visual_embedding_dim""": 2048}
          elif "vqa" in checkpoint_path:
               UpperCAmelCase__      :  str				      =			{"""visual_embedding_dim""": 2048}
          elif "nlvr" in checkpoint_path:
               UpperCAmelCase__      :  Optional[Any]				      =			{"""visual_embedding_dim""": 1024}
          else:
               raise NotImplementedError(F"No implementation found for `{checkpoint_path}`."       )
     else:
          if "vcr" in checkpoint_path:
               UpperCAmelCase__      :  str				      =			{"""visual_embedding_dim""": 512}
               UpperCAmelCase__      :  Optional[int]				      =			"""multichoice"""
          elif "vqa_advanced" in checkpoint_path:
               UpperCAmelCase__      :  Union[str, Any]				      =			{"""visual_embedding_dim""": 2048}
               UpperCAmelCase__      :  Tuple				      =			"""vqa_advanced"""
          elif "vqa" in checkpoint_path:
               UpperCAmelCase__      :  List[Any]				      =			{"""visual_embedding_dim""": 2048, """num_labels""": 3129}
               UpperCAmelCase__      :  List[Any]				      =			"""vqa"""
          elif "nlvr" in checkpoint_path:
               UpperCAmelCase__      :  Union[str, Any]				      =			{
                   """visual_embedding_dim""": 1024,
                   """num_labels""": 2,
               }
               UpperCAmelCase__      :  str				      =			"""nlvr"""

     UpperCAmelCase__      :  Union[str, Any]				      =			VisualBertConfig(**lowercase_       )

     # Load State Dict
     UpperCAmelCase__      :  int				      =			load_state_dict(lowercase_       )

     UpperCAmelCase__      :  str				      =			get_new_dict(lowercase_ ,							lowercase_       )

     if model_type == "pretraining":
          UpperCAmelCase__      :  Union[str, Any]				      =			VisualBertForPreTraining(lowercase_       )
     elif model_type == "vqa":
          UpperCAmelCase__      :  Optional[int]				      =			VisualBertForQuestionAnswering(lowercase_       )
     elif model_type == "nlvr":
          UpperCAmelCase__      :  List[Any]				      =			VisualBertForVisualReasoning(lowercase_       )
     elif model_type == "multichoice":
          UpperCAmelCase__      :  List[str]				      =			VisualBertForMultipleChoice(lowercase_       )

     model.load_state_dict(lowercase_       )
     # Save Checkpoints
     Path(lowercase_       ).mkdir(exist_ok=lowercase_       )
     model.save_pretrained(lowercase_       )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:							List[Any] 				=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
      parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
      SCREAMING_SNAKE_CASE__		:							Optional[int] 				=				parser.parse_args()
      convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)














'''simple docstring'''







import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = False   ,	UpperCamelCase__ = False   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         super().__init__(
             UpperCamelCase__   ,	split=UpperCamelCase__   ,	features=UpperCamelCase__   ,	cache_dir=UpperCamelCase__   ,	keep_in_memory=UpperCamelCase__   ,	streaming=UpperCamelCase__   ,	num_proc=UpperCamelCase__   ,	**UpperCamelCase__   ,	)
         a_						     =			field
         a_						     =			path_or_paths if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else {self.split: path_or_paths}
         a_						     =			Json(
             cache_dir=UpperCamelCase__   ,	data_files=UpperCamelCase__   ,	features=UpperCamelCase__   ,	field=UpperCamelCase__   ,	**UpperCamelCase__   ,	)







  def    _a       (			self	):






         """simple docstring"""


         if self.streaming:
                a_						     =			self.builder.as_streaming_dataset(split=self.split	)
         # Build regular (map-style) dataset
         else:
                a_						     =			None
                a_						     =			None
                a_						     =			None
                a_						     =			None

                self.builder.download_and_prepare(
                    download_config=UpperCamelCase__   ,	download_mode=UpperCamelCase__   ,	verification_mode=UpperCamelCase__   ,	base_path=UpperCamelCase__   ,	num_proc=self.num_proc   ,	)
                a_						     =			self.builder.as_dataset(
                    split=self.split   ,	verification_mode=UpperCamelCase__   ,	in_memory=self.keep_in_memory	)
         return dataset





class   __SCREAMING_SNAKE_CASE       :


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         if num_proc is not None and num_proc <= 0:
                raise ValueError(f'num_proc {num_proc} must be an integer > 0.'	)

         a_						     =			dataset
         a_						     =			path_or_buf
         a_						     =			batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         a_						     =			num_proc
         a_						     =			'utf-8'
         a_						     =			to_json_kwargs







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.to_json_kwargs.pop('path_or_buf'   ,	UpperCamelCase__	)
         a_						     =			self.to_json_kwargs.pop('orient'   ,	'records'	)
         a_						     =			self.to_json_kwargs.pop('lines'   ,	True if orient == 'records' else False	)
         a_						     =			self.to_json_kwargs.pop('index'   ,	False if orient in ['split', 'table'] else True	)
         a_						     =			self.to_json_kwargs.pop('compression'   ,	UpperCamelCase__	)

         if compression not in [None, "infer", "gzip", "bz2", "xz"]:
                raise NotImplementedError(f'`datasets` currently does not support {compression} compression'	)

         if isinstance(self.path_or_buf   ,	(str, bytes, os.PathLike)	):
                with fsspec.open(self.path_or_buf   ,	'wb'   ,	compression=UpperCamelCase__	) as buffer:
                       a_						     =			self._write(file_obj=UpperCamelCase__   ,	orient=UpperCamelCase__   ,	lines=UpperCamelCase__   ,	index=UpperCamelCase__   ,	**self.to_json_kwargs	)
         else:
                if compression:
                       raise NotImplementedError(
                           f'The compression parameter is not supported when writing to a buffer, but compression={compression}'
                           ' was passed. Please provide a local path instead.'	)
                a_						     =			self._write(
                    file_obj=self.path_or_buf   ,	orient=UpperCamelCase__   ,	lines=UpperCamelCase__   ,	index=UpperCamelCase__   ,	**self.to_json_kwargs	)
         return written







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_     ,	a_     ,	a_     ,	a_     ,	a_						     =			args

         a_						     =			query_table(
             table=self.dataset.data   ,	key=slice(UpperCamelCase__   ,	offset + self.batch_size	)   ,	indices=self.dataset._indices   ,	)
         a_						     =			batch.to_pandas().to_json(
             path_or_buf=UpperCamelCase__   ,	orient=UpperCamelCase__   ,	lines=UpperCamelCase__   ,	index=UpperCamelCase__   ,	**UpperCamelCase__	)
         if not json_str.endswith('\n'	):
                json_str += "\n"
         return json_str.encode(self.encoding	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			0

         if self.num_proc is None or self.num_proc == 1:
                for offset in logging.tqdm(
                    range(0   ,	len(self.dataset	)   ,	self.batch_size	)   ,	unit='ba'   ,	disable=not logging.is_progress_bar_enabled()   ,	desc='Creating json from Arrow format'   ,	):
                       a_						     =			self._batch_json((offset, orient, lines, index, to_json_kwargs)	)
                       written += file_obj.write(UpperCamelCase__	)
         else:
                a_     ,	a_						     =			len(self.dataset	), self.batch_size
                with multiprocessing.Pool(self.num_proc	) as pool:
                       for json_str in logging.tqdm(
                           pool.imap(
                               self._batch_json   ,	[(offset, orient, lines, index, to_json_kwargs) for offset in range(0   ,	UpperCamelCase__   ,	UpperCamelCase__	)]   ,	)   ,	total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size   ,	unit='ba'   ,	disable=not logging.is_progress_bar_enabled()   ,	desc='Creating json from Arrow format'   ,	):
                              written += file_obj.write(UpperCamelCase__	)

         return written




